ECOC, borrowed originally from the communication theory [1]}, [2]}, is a multi-class classification ensemble, in which a given multi-class problem is decomposed into several two-class problems, whose simpler decision boundaries are then combined to give the final, more complex decision boundary. The errors of the base classifiers that implement the two-class decision boundaries are corrected to a certain degree [3]}.
Several data-dependent and independent approaches can be used for guiding the decomposition process [4]}, [5]}.
In [6]}, it has been theoretically and experimentally demonstrated that ECOC frameworks formed using random class splits obtain close to Bayes performance, if there are infinitely many such splits, and the associated base classifiers achieve good accuracy. In practice, the performance reaches the optimum very rapidly [7]} as the number of the classifiers increases. The superiority of this method, which we refer to as randECOC, over the rest of the data independent and dependent ECOC approaches is demonstrated in [3]}, [6]}, [10]}.
